R&D PhD Summer Intern – Machine Learning / AI

Procter & Gamble

4d•$61 - $61•Onsite

About The Position

Research and Development (R&D) at Procter & Gamble, the largest consumer packaged goods company in the world, includes a diverse group of roles that contribute to the innovation and development of our products. It encompasses roles in product research, formulation, testing, and scientific analysis. You will find variety and excitement starting Day 1. This internship is specifically designed for individuals working towards a PhD who are developing proficiency in their field. As an intern or co-op in management, you will have the opportunity to learn from experienced professionals in a supportive environment. This is a 12-week paid internship, designed to provide you with a solid foundation for future career growth. The internship will take place from May (potentially early June) to August of 2026. Join us at P&G, where your contributions will play a vital role in shaping the future of consumer products! The Opportunity: P&G has an opportunity for a PhD intern to work in our Corporate R&D to develop cutting-edge machine learning models that touch and improve consumers' daily lives. The ideal candidate will demonstrate a strong eagerness to learn and grow professionally and possess excellent communication skills—both written and verbal. This role is perfect for those with passion for innovation and problem-solving, along with a proactive attitude and the ability to adapt to new challenges. Join us in this dynamic environment, where your contributions will make a real impact as part of a collaborative team!

Requirements

Education: Working towards a PhD in Computer Science, Computer Engineering, Data Science, or related fields.
Available to work a 12-week internship from May/early June to August in the summer of 2026.
Available to work 5 days a week in office at Mason, OH location.
Experience in developing and deploying machine learning and deep learning models.
Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.

Nice To Haves

Experience in time series data modeling.
Experience in multi-GPU training.
Demonstrated a proven track record of significant contributions through grants, fellowships, patents, or publications in leading machine learning workshops, journals, or conferences.
Experience in self-supervised or semi-supervised learning methods.
Familiarity with managing large datasets.
Proficiency in data management tools (e.g., SQL, Pandas, Spark).
Proficiency in Git/GitHub for version control, familiarity with CI/CD tools for automated deployment, and ability to create and maintain clear documentation

Responsibilities

Explore existing literature and techniques in self-supervised and semi-supervised learning to devise a strategy for using large unlabeled datasets to improve time-series data modeling.
Develop a functional pipeline that demonstrates improved performance in time-series data modeling (e.g., classification) using available unlabeled datasets.
Design and implement robust and efficient data pipelines to effectively manage and handle large volumes of data for machine learning training tasks.
Share key insights from your research and development process with the broader team to improve our collective understanding of self-supervised and semi-supervised learning strategies.

Benefits

Responsibilities as of Day 1 - you will feel the ownership of your work from the beginning, and you will be given specific ownership areas and responsibilities.
Continuous mentorship - you will work with passionate people and receive formal training as well as day-to-day mentoring from your manager.
Work and be part of a dynamic and encouraging environment - working over a diverse array of interesting problems.
Promote agility and work/life effectiveness and your long-term well-being.

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